{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "id": "9GRMF7YIMY8e"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.backends.backend_pdf import PdfPages"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Data"
      ],
      "metadata": {
        "id": "bYUhFkeNMpAj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data=np.load('s1_train.npy',allow_pickle=True)\n",
        "test_data=np.load('s1_test.npy',allow_pickle=True)"
      ],
      "metadata": {
        "id": "WF_FtJS5MsxT"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## plot some PPG signal from s1_train dataset"
      ],
      "metadata": {
        "id": "2MjrP0CYM2Yj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_plots = 4\n",
        "fig_size = (9, 9)\n",
        "\n",
        "# Calculate the number of rows and columns for the subplot grid\n",
        "num_rows = num_plots\n",
        "num_cols = num_plots\n",
        "\n",
        "plt.figure(figsize=fig_size)\n",
        "\n",
        "for plot_counter in range(1, num_plots * num_plots + 1):\n",
        "    plt.subplot(num_rows, num_cols, plot_counter)\n",
        "    plt.plot(train_data[plot_counter - 1][0], color='b')\n",
        "\n",
        "plt.suptitle(\"PPG signal\")\n",
        "plt.tight_layout()\n",
        "plt.savefig('output.png', dpi=1200, format='png', bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "id": "ghlB8Ieb5Fk6",
        "outputId": "6072c002-f901-4686-989d-851322e6cb15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  PDF Report- plot all signals in the pdf file"
      ],
      "metadata": {
        "id": "AzObtU8wSRRY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is to check all signals and detect any abnormal signals."
      ],
      "metadata": {
        "id": "tcIO8ma-6Z6_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_plots = 48\n",
        "num_rows = 4\n",
        "num_cols = 5\n",
        "\n",
        "with PdfPages(r'ppg_phase1.pdf') as export_pdf:\n",
        "    for t in range(num_plots):\n",
        "        fig = plt.figure(figsize=(12, 12))\n",
        "        plt.suptitle(\"PPG signal\")\n",
        "\n",
        "        for c, s in enumerate(range(t * num_rows * num_cols, (t + 1) * num_rows * num_cols)):\n",
        "            i, j = divmod(c, num_cols)\n",
        "            plt.subplot(num_rows, num_cols, c + 1)\n",
        "            plt.plot(train_data[s][0], color='b')\n",
        "            plt.title(s)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        export_pdf.savefig()\n",
        "        plt.close()"
      ],
      "metadata": {
        "id": "9iPq7YOm6Sdg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing"
      ],
      "metadata": {
        "id": "FHFBJ_BdWMFG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalize Signal"
      ],
      "metadata": {
        "id": "xWNd7cC1Wgt2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loop over each row (i) in data_train\n",
        "for i in range(train_data.shape[0]):\n",
        "    min_val = np.min(train_data[i][0])\n",
        "    max_val = np.max(train_data[i][0])\n",
        "\n",
        "    # Check if max and min are not equal (avoid division by zero)\n",
        "    if max_val != min_val:\n",
        "        train_data[i][0] = (train_data[i][0] - min_val) / (max_val - min_val)"
      ],
      "metadata": {
        "id": "1gsMKauK-uFl"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### delet short signal (length<150 ms)"
      ],
      "metadata": {
        "id": "iw-6d_PXWkAV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list of indices to remove\n",
        "short_signal = [i for i in range(960) if len(train_data[i][0]) < 150]\n",
        "\n",
        "# Remove the selected indices from the numpy array\n",
        "data_train = np.delete(train_data, short_signal, axis=0)"
      ],
      "metadata": {
        "id": "hW3r-xlK_c3S"
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szovmBQHsKl5",
        "outputId": "39a68dd6-a401-4765-a1cc-89c2d6ae3e86"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Base-line **Model**"
      ],
      "metadata": {
        "id": "eO3DVbbGW06V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean of data_train[i][1] using NumPy\n",
        "y = np.mean(train_data[:, 1])\n",
        "\n",
        "print('Y:', y)\n",
        "\n",
        "# Calculate the baseline RMSE using NumPy\n",
        "base_line_rmse = np.sqrt(np.mean((y - train_data[:, 1]) ** 2))\n",
        "\n",
        "print('Base line RMSE:', base_line_rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6tjS6IoA5UE",
        "outputId": "d60777cf-3bac-4f2a-9602-9b4481df3c00"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y: 131.06994981998724\n",
            "Base line RMSE: 25.7095357794773\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Main Model"
      ],
      "metadata": {
        "id": "0KJP7RRFXNJd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Upsampling - set signals' length to 200 ms"
      ],
      "metadata": {
        "id": "7AjbNtdaXPt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def upsample_signal(signal, target_length):\n",
        "    # Calculate the factor by which to upsample the signal\n",
        "    upsample_factor = target_length / len(signal)\n",
        "\n",
        "    # Create an array with the target length\n",
        "    upsampled_signal = np.zeros(target_length)\n",
        "\n",
        "    # Linearly interpolate the values\n",
        "    for i in range(target_length):\n",
        "        index = i / upsample_factor\n",
        "        lower_index = int(index)\n",
        "        upper_index = min(lower_index + 1, len(signal) - 1)\n",
        "        fractional_part = index - lower_index\n",
        "\n",
        "        if lower_index == upper_index:\n",
        "            upsampled_signal[i] = signal[lower_index]\n",
        "        else:\n",
        "            upsampled_signal[i] = (1 - fractional_part) * signal[lower_index] + fractional_part * signal[upper_index]\n",
        "\n",
        "    return upsampled_signal"
      ],
      "metadata": {
        "id": "4KGpXrEN1yU-"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_length = 200\n",
        "for i in range(np.shape(train_data)[0]):\n",
        "\n",
        "  train_data[i][0] = upsample_signal(train_data[i][0], target_length)"
      ],
      "metadata": {
        "id": "AVG_SPGv13LE"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_length = 200\n",
        "for i in range(np.shape(test_data)[0]):\n",
        "\n",
        "  test_data[i][0] = upsample_signal(test_data[i][0], target_length)"
      ],
      "metadata": {
        "id": "-f_gqW1cCEwn"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## linear regression"
      ],
      "metadata": {
        "id": "pyMPMyPHXY39"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Split dataset"
      ],
      "metadata": {
        "id": "SYqO-DO3DM72"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = np.ones((np.shape(train_data)[0]))\n",
        "X_train = np.ones((np.shape(train_data)[0],200))\n",
        "\n",
        "for i in range(np.shape(train_data)[0]):\n",
        "   y_train[i] = train_data[i][1]\n",
        "   X_train[i][:] = train_data[i][0]"
      ],
      "metadata": {
        "id": "DXt9ogye6l_i"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = np.ones((np.shape(test_data)[0],200))\n",
        "\n",
        "for i in range(np.shape(test_data)[0]):\n",
        "   X_test[i] = test_data[i][0]"
      ],
      "metadata": {
        "id": "IBkBYJA9_gJn"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define and fit the linear regression model"
      ],
      "metadata": {
        "id": "_M-0cpFDDCb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import math\n",
        "\n",
        "\n",
        "# Initialize the linear regression model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model on the training data\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "#Make predictions on the test data\n",
        "y_train_pred = model.predict(X_train)\n",
        "\n",
        "# Calculate Mean Squared Error (MSE)\n",
        "mse = mean_squared_error(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Root Mean Square Error (RMSE)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print(\"MSE:\", mse)\n",
        "print(\"RMSE:\", rmse)\n",
        "\n",
        "#Make predictions on the test data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "file_name = \"linear_regression_prediction.txt\"\n",
        "\n",
        "# Save the array to a text file\n",
        "np.savetxt(file_name, y_pred, fmt='%d', delimiter='\\t')\n",
        "\n",
        "print(f\"Array saved to {file_name}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_6PbrreEDpa",
        "outputId": "b0081695-d099-4567-a53d-fba59b2c3a2c"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 472.9579600685149\n",
            "RMSE: 21.747596650400588\n",
            "Array saved to linear_regression_prediction.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ridge Regression"
      ],
      "metadata": {
        "id": "F_jXn8YZ3280"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.linear_model import Ridge\n",
        "from numpy import arange\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# define model\n",
        "model = Ridge(alpha=1.0)\n",
        "# define model evaluation method\n",
        "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "grid = dict()\n",
        "grid['alpha'] = arange(0, 1, 0.01)\n",
        "# define search\n",
        "search = GridSearchCV(model, grid, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
        "# perform the search\n",
        "model = search.fit(X_train, y_train)\n",
        "\n",
        "#Make predictions on the test data\n",
        "y_train_pred = model.predict(X_train)\n",
        "\n",
        "# Calculate Mean Squared Error (MSE)\n",
        "mse = mean_squared_error(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Root Mean Square Error (RMSE)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print(\"MSE:\", mse)\n",
        "print(\"RMSE:\", rmse)\n",
        "\n",
        "#Make predictions on the test data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "file_name = \"ridge_regression_prediction.txt\"\n",
        "\n",
        "# Save the array to a text file\n",
        "np.savetxt(file_name, y_pred, fmt='%d', delimiter='\\t')\n",
        "\n",
        "print(f\"Array saved to {file_name}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8PnF1H_hAA-",
        "outputId": "2acb0a03-8f3c-401f-cbf6-40afc807e7a2"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 589.7135873790749\n",
            "RMSE: 24.284019176797624\n",
            "Array saved to ridge_regression_prediction.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lasso"
      ],
      "metadata": {
        "id": "aNVqQX9C4Cxa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold, GridSearchCV\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "# Define the Lasso regression model\n",
        "lasso_model = Lasso()\n",
        "\n",
        "# Define the parameter grid for grid search\n",
        "param_grid = {\n",
        "    'alpha': [0.01, 0.1, 1.0, 10.0]\n",
        "}\n",
        "\n",
        "# Create a k-fold cross-validation strategy\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "# Create a GridSearchCV object\n",
        "grid_search = GridSearchCV(estimator=lasso_model, param_grid=param_grid,\n",
        "                           scoring='neg_mean_squared_error', cv=kf)\n",
        "\n",
        "# Fit the model with grid search to find the best hyperparameters\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best hyperparameters\n",
        "best_alpha = grid_search.best_params_['alpha']\n",
        "print(f\"Best alpha: {best_alpha}\")\n",
        "\n",
        "# Get the best model\n",
        "best_model = grid_search.best_estimator_\n",
        "\n",
        "# Fit the best model on the full training data\n",
        "best_model.fit(X_train, y_train)\n",
        "\n",
        "#Make predictions on the test data\n",
        "y_train_pred = model.predict(X_train)\n",
        "\n",
        "# Calculate Mean Squared Error (MSE)\n",
        "mse = mean_squared_error(y_train, y_train_pred)\n",
        "\n",
        "# Calculate Root Mean Square Error (RMSE)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print(\"MSE:\", mse)\n",
        "print(\"RMSE:\", rmse)\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred = best_model.predict(X_test)\n",
        "\n",
        "file_name = \"lasso_prediction.txt\"\n",
        "\n",
        "# Save the array to a text file\n",
        "np.savetxt(file_name, y_pred, fmt='%d', delimiter='\\t')\n",
        "\n",
        "print(f\"Array saved to {file_name}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhZZfYFoGbWs",
        "outputId": "716ec0b6-a8e2-49fc-c4bd-72b419ecee08"
      },
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.306e+04, tolerance: 5.279e+01\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.724e+04, tolerance: 5.167e+01\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.476e+04, tolerance: 5.369e+01\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.488e+04, tolerance: 5.386e+01\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.029e+04, tolerance: 5.234e+01\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.027e+04, tolerance: 6.610e+01\n",
            "  model = cd_fast.enet_coordinate_descent(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best alpha: 0.01\n",
            "MSE: 589.7135873790749\n",
            "RMSE: 24.284019176797624\n",
            "Array saved to lasso_prediction.txt\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.027e+04, tolerance: 6.610e+01\n",
            "  model = cd_fast.enet_coordinate_descent(\n"
          ]
        }
      ]
    }
  ]
}